Tİ-6AL-4V ALAŞIMININ FREZELENMESİNDE YÜZEY PÜRÜZLÜLÜĞÜNÜN REGRESYON ANALİZİ İLE MODELLENMESİ

Author:

TOPRAK İnayet Burcu1,ÇOLAK Oğuz2,BAYHAN Mustafa3

Affiliation:

1. Akdeniz Üniversitesi

2. Eskişehir Teknik Üniversitesi

3. SÜLEYMAN DEMİREL ÜNİVERSİTESİ

Abstract

In this study, Ti-6Al-4V was machined under high pressure cooling conditions. Cutting parameters which were assumed as independent variables are consist of 4 different levels of cutting speed (Vc: 50-70-90-110 m/min), feed rate (f: 0.05-0.1-0.15-0.2 mm/rev) and cutting fluid pressure (P: 6-100-200-300 bar). By using SPSS 20 software, regression equations of surface roughness relative to cutting parameters was obtained as linear, second degree and linear logarithmic. Second degree multiple regression model showed best results of estimation. In the model, 95 percent of the surface roughness alterations can be explained by independent variables. Correlation between experimental data and the model was calculated as 0.975. As a result, second degree regression model proved to be successful in predicting surface roughness. The result of the study confirms the literature. When models are compared the most important parameter that affects surface roughness was observed as the feed rate. The results of the study confirms the literature.

Publisher

Muhendislik Bilimleri ve Tasarim Dergisi

Subject

General Medicine

Reference33 articles.

1. Akkuş, H., 2010. Prediction of Surface Roughness in Turning Operations Using Artificial Intelligence and Statistical Methods, MSc. Thesis, Selçuk University, Konya.

2. Akkuş, H., Asiltürk, İ., 2011. Predicting Surface Roughness of AISI 4140 Steel in Hard Turning Process through Artificial Neural Network, Fuzzy Logic and Regression Models, Scientific Research and Essays, 6(13), 2729-2736.

3. Akkuş H., Yaka H., Uğur L., 2017. Creating The Mathematical Model for The Surface Roughness Values Occurring During The Turning of The AISI1040 Steel, Sigma Journal of Engineering and Natural Sciences, 35 (2), 303-310.

4. Akkuş, H., 2021. Investigation of Surface Roughness Values During Machinability of AISI 1040 Steel With Different Estimation Models, Kahramanmaras Sutcu Imam University Journal of Engineering Sciences, 24 (2), 84-92.

5. Arokiadass, R., Palaniradja, K., Alagumoorthi, N., 2011. Surface Roughness Prediction Model in End Milling of Al/SiCp MMC by Carbide Tools, International Journal of Engineering, Science and Technology, 3(6), 78-87.

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